Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations70511
Missing cells457687
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory223.3 MiB
Average record size in memory3.2 KiB

Variable types

Text17
Categorical19
Numeric3

Alerts

etapa_autorizacion_pliego has constant value "Autorización del pliego" Constant
etapa_autorizacion_llamado has constant value "Autorización de llamado" Constant
etapa_acto_apertura has constant value "Acto de apertura" Constant
apartado is highly overall correlated with financiamiento_externo and 2 other fieldsHigh correlation
cro_cant_dias_publicar is highly overall correlated with genera_recursos_mediante and 1 other fieldsHigh correlation
encuadre_legal is highly overall correlated with financiamiento_externo and 4 other fieldsHigh correlation
etapa is highly overall correlated with etapa_lic and 2 other fieldsHigh correlation
etapa_lic is highly overall correlated with etapa and 2 other fieldsHigh correlation
financiamiento_externo is highly overall correlated with apartado and 4 other fieldsHigh correlation
genera_recursos is highly overall correlated with encuadre_legal and 4 other fieldsHigh correlation
genera_recursos_mediante is highly overall correlated with apartado and 8 other fieldsHigh correlation
modalidad is highly overall correlated with financiamiento_externo and 1 other fieldsHigh correlation
ofertas_confirmadas is highly overall correlated with proveedores_participantesHigh correlation
proveedores_participantes is highly overall correlated with ofertas_confirmadasHigh correlation
tipo_de_bienes is highly overall correlated with cro_cant_dias_publicar and 8 other fieldsHigh correlation
tipo_doc_compra is highly overall correlated with encuadre_legal and 5 other fieldsHigh correlation
tipo_proceso is highly overall correlated with apartado and 5 other fieldsHigh correlation
etapa is highly imbalanced (99.2%) Imbalance
modalidad is highly imbalanced (81.3%) Imbalance
moneda is highly imbalanced (88.5%) Imbalance
tipo_doc_compra is highly imbalanced (88.1%) Imbalance
requiere_pago is highly imbalanced (98.3%) Imbalance
apartado is highly imbalanced (59.1%) Imbalance
etapa_lic is highly imbalanced (99.2%) Imbalance
genera_recursos is highly imbalanced (92.6%) Imbalance
financiamiento_externo is highly imbalanced (97.3%) Imbalance
genera_recursos_mediante is highly imbalanced (52.2%) Imbalance
num_expediente has 825 (1.2%) missing values Missing
etapa has 825 (1.2%) missing values Missing
modalidad has 825 (1.2%) missing values Missing
moneda has 841 (1.2%) missing values Missing
encuadre_legal has 825 (1.2%) missing values Missing
cotizacion has 825 (1.2%) missing values Missing
tipo_doc_compra has 825 (1.2%) missing values Missing
lugar_recepcion has 1400 (2.0%) missing values Missing
plazo_oferta has 825 (1.2%) missing values Missing
requiere_pago has 825 (1.2%) missing values Missing
apartado has 33215 (47.1%) missing values Missing
etapa_lic has 825 (1.2%) missing values Missing
etapa_autorizacion_pliego has 43649 (61.9%) missing values Missing
etapa_autorizacion_llamado has 43703 (62.0%) missing values Missing
etapa_acto_apertura has 34105 (48.4%) missing values Missing
genera_recursos has 825 (1.2%) missing values Missing
financiamiento_externo has 825 (1.2%) missing values Missing
acepta_prorroga has 1235 (1.8%) missing values Missing
tipo_de_bienes has 69880 (99.1%) missing values Missing
genera_recursos_mediante has 69880 (99.1%) missing values Missing
cro_fecha_publicacion has 898 (1.3%) missing values Missing
cro_fecha_inicio_consultas has 898 (1.3%) missing values Missing
cro_fecha_final_consultas has 898 (1.3%) missing values Missing
cro_cant_dias_publicar has 61808 (87.7%) missing values Missing
cro_fecha_inicio_recepcion_documentos has 40417 (57.3%) missing values Missing
cro_fecha_fin_recepcion_documentos has 40417 (57.3%) missing values Missing
cro_fecha_acto_apertura has 898 (1.3%) missing values Missing
inicio_contrato has 843 (1.2%) missing values Missing
duracion_contrato has 1235 (1.8%) missing values Missing
proveedores_participantes has 1196 (1.7%) missing values Missing
ofertas_confirmadas has 1196 (1.7%) missing values Missing
num_proceso has unique values Unique
proveedores_participantes has 2982 (4.2%) zeros Zeros
ofertas_confirmadas has 5839 (8.3%) zeros Zeros

Reproduction

Analysis started2025-03-22 19:32:48.415886
Analysis finished2025-03-22 19:33:29.165042
Duration40.75 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

num_proceso
Text

Unique 

Distinct70511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
2025-03-22T16:33:29.504419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length14.626484
Min length13

Characters and Unicode

Total characters1031328
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70511 ?
Unique (%)100.0%

Sample

1st row23-0009-LPR16
2nd row23-0010-LPR16
3rd row23-0011-LPR16
4th row23-0012-LPR16
5th row23-0014-LPU16
ValueCountFrequency (%)
23-0044-cdi16 1
 
< 0.1%
98-0027-cdi22 1
 
< 0.1%
23-0009-lpr16 1
 
< 0.1%
23-0010-lpr16 1
 
< 0.1%
23-0011-lpr16 1
 
< 0.1%
23-0012-lpr16 1
 
< 0.1%
23-0014-lpu16 1
 
< 0.1%
23-0015-lpu16 1
 
< 0.1%
23-0017-lpu16 1
 
< 0.1%
23-0018-lpu16 1
 
< 0.1%
Other values (70501) 70501
> 99.9%
2025-03-22T16:33:30.227701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 145601
14.1%
- 141022
13.7%
1 104193
 
10.1%
2 97999
 
9.5%
4 59851
 
5.8%
8 50142
 
4.9%
3 49064
 
4.8%
/ 38417
 
3.7%
C 38168
 
3.7%
D 37663
 
3.7%
Other values (12) 269208
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1031328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 145601
14.1%
- 141022
13.7%
1 104193
 
10.1%
2 97999
 
9.5%
4 59851
 
5.8%
8 50142
 
4.9%
3 49064
 
4.8%
/ 38417
 
3.7%
C 38168
 
3.7%
D 37663
 
3.7%
Other values (12) 269208
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1031328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 145601
14.1%
- 141022
13.7%
1 104193
 
10.1%
2 97999
 
9.5%
4 59851
 
5.8%
8 50142
 
4.9%
3 49064
 
4.8%
/ 38417
 
3.7%
C 38168
 
3.7%
D 37663
 
3.7%
Other values (12) 269208
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1031328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 145601
14.1%
- 141022
13.7%
1 104193
 
10.1%
2 97999
 
9.5%
4 59851
 
5.8%
8 50142
 
4.9%
3 49064
 
4.8%
/ 38417
 
3.7%
C 38168
 
3.7%
D 37663
 
3.7%
Other values (12) 269208
26.1%
Distinct66998
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-03-22T16:33:30.655328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length41
Mean length33.608628
Min length29

Characters and Unicode

Total characters2369778
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63747 ?
Unique (%)90.4%

Sample

1st rowEX-2016-00697885- -APN-DPYS#SGP
2nd rowEX-2016-01031683- -APN-DPYS#SGP
3rd rowEX-2016-01358346- -APN-DDMYA#SGP
4th rowEX-2016-01392005- -APN-DDMYA#SGP
5th rowEX-2016-00474707- -APN-DPYS#SGP
ValueCountFrequency (%)
apn-dcon#faa 2001
 
1.4%
apn-dgit#ara 1433
 
1.0%
apn-dcyc#mc 1118
 
0.8%
apn-dacmysg#anlis 1011
 
0.7%
apn-dc#hp 965
 
0.7%
apn-dcyc#mds 851
 
0.6%
apn-gaen#cnea 817
 
0.6%
apn-da#inidep 795
 
0.6%
apn-gasnya#cnea 791
 
0.6%
apn-dmza#dnv 788
 
0.6%
Other values (67570) 130452
92.5%
2025-03-22T16:33:31.284473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 352555
14.9%
211533
 
8.9%
2 184143
 
7.8%
A 161325
 
6.8%
0 143232
 
6.0%
N 113347
 
4.8%
1 110567
 
4.7%
E 102721
 
4.3%
P 86305
 
3.6%
X 71297
 
3.0%
Other values (27) 832753
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2369778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 352555
14.9%
211533
 
8.9%
2 184143
 
7.8%
A 161325
 
6.8%
0 143232
 
6.0%
N 113347
 
4.8%
1 110567
 
4.7%
E 102721
 
4.3%
P 86305
 
3.6%
X 71297
 
3.0%
Other values (27) 832753
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2369778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 352555
14.9%
211533
 
8.9%
2 184143
 
7.8%
A 161325
 
6.8%
0 143232
 
6.0%
N 113347
 
4.8%
1 110567
 
4.7%
E 102721
 
4.3%
P 86305
 
3.6%
X 71297
 
3.0%
Other values (27) 832753
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2369778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 352555
14.9%
211533
 
8.9%
2 184143
 
7.8%
A 161325
 
6.8%
0 143232
 
6.0%
N 113347
 
4.8%
1 110567
 
4.7%
E 102721
 
4.3%
P 86305
 
3.6%
X 71297
 
3.0%
Other values (27) 832753
35.1%
Distinct61474
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
2025-03-22T16:33:31.839878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length253
Median length133
Mean length62.620102
Min length5

Characters and Unicode

Total characters4415406
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56920 ?
Unique (%)80.7%

Sample

1st rowAdquisición de elementos de electricidad
2nd rowAdquisición de elementos de plomería y cerrajería.
3rd rowADQUISICIÓN INSUMOS PARA BAÑOS
4th rowServicio anual de mantenimiento, y controles mensuales de Extintores, y adquisición
5th rowAdquisición de indumentaria.
ValueCountFrequency (%)
de 110612
 
17.1%
para 28055
 
4.3%
y 26228
 
4.0%
adquisición 24671
 
3.8%
servicio 16412
 
2.5%
la 12617
 
1.9%
del 11406
 
1.8%
el 10373
 
1.6%
7567
 
1.2%
mantenimiento 7017
 
1.1%
Other values (26080) 393690
60.7%
2025-03-22T16:33:33.021823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
581644
 
13.2%
A 277085
 
6.3%
E 268398
 
6.1%
I 247226
 
5.6%
R 164898
 
3.7%
e 162774
 
3.7%
S 160160
 
3.6%
O 160147
 
3.6%
N 151733
 
3.4%
i 150739
 
3.4%
Other values (124) 2090602
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4415406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
581644
 
13.2%
A 277085
 
6.3%
E 268398
 
6.1%
I 247226
 
5.6%
R 164898
 
3.7%
e 162774
 
3.7%
S 160160
 
3.6%
O 160147
 
3.6%
N 151733
 
3.4%
i 150739
 
3.4%
Other values (124) 2090602
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4415406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
581644
 
13.2%
A 277085
 
6.3%
E 268398
 
6.1%
I 247226
 
5.6%
R 164898
 
3.7%
e 162774
 
3.7%
S 160160
 
3.6%
O 160147
 
3.6%
N 151733
 
3.4%
i 150739
 
3.4%
Other values (124) 2090602
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4415406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
581644
 
13.2%
A 277085
 
6.3%
E 268398
 
6.1%
I 247226
 
5.6%
R 164898
 
3.7%
e 162774
 
3.7%
S 160160
 
3.6%
O 160147
 
3.6%
N 151733
 
3.4%
i 150739
 
3.4%
Other values (124) 2090602
47.3%

tipo_proceso
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
Contratación Directa
37641 
Licitación Privada
23220 
Licitación Pública
8716 
Subasta Pública
 
407
Concurso Privado
 
277
Other values (2)
 
250

Length

Max length20
Median length20
Mean length19.03659
Min length15

Characters and Unicode

Total characters1342289
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLicitación Privada
2nd rowLicitación Privada
3rd rowLicitación Privada
4th rowLicitación Privada
5th rowLicitación Pública

Common Values

ValueCountFrequency (%)
Contratación Directa 37641
53.4%
Licitación Privada 23220
32.9%
Licitación Pública 8716
 
12.4%
Subasta Pública 407
 
0.6%
Concurso Privado 277
 
0.4%
Concurso Público 222
 
0.3%
Compulsa de Precios 28
 
< 0.1%

Length

2025-03-22T16:33:33.238768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:33.447547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
contratación 37641
26.7%
directa 37641
26.7%
licitación 31936
22.6%
privada 23220
16.5%
pública 9123
 
6.5%
concurso 499
 
0.4%
subasta 407
 
0.3%
privado 277
 
0.2%
público 222
 
0.2%
compulsa 28
 
< 0.1%
Other values (2) 56
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 203960
15.2%
a 201541
15.0%
c 149026
11.1%
t 145266
10.8%
n 107717
8.0%
r 99306
7.4%
70539
 
5.3%
ó 69577
 
5.2%
o 39194
 
2.9%
C 38168
 
2.8%
Other values (14) 217995
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342289
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 203960
15.2%
a 201541
15.0%
c 149026
11.1%
t 145266
10.8%
n 107717
8.0%
r 99306
7.4%
70539
 
5.3%
ó 69577
 
5.2%
o 39194
 
2.9%
C 38168
 
2.8%
Other values (14) 217995
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342289
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 203960
15.2%
a 201541
15.0%
c 149026
11.1%
t 145266
10.8%
n 107717
8.0%
r 99306
7.4%
70539
 
5.3%
ó 69577
 
5.2%
o 39194
 
2.9%
C 38168
 
2.8%
Other values (14) 217995
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342289
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 203960
15.2%
a 201541
15.0%
c 149026
11.1%
t 145266
10.8%
n 107717
8.0%
r 99306
7.4%
70539
 
5.3%
ó 69577
 
5.2%
o 39194
 
2.9%
C 38168
 
2.8%
Other values (14) 217995
16.2%
Distinct15460
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2025-03-22T16:33:33.901068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1480731
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5622 ?
Unique (%)8.0%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 70511
33.3%
10:00 18940
 
9.0%
12:00 11163
 
5.3%
11:00 10552
 
5.0%
09:00 8708
 
4.1%
08:00 3955
 
1.9%
13:00 3862
 
1.8%
15:00 2668
 
1.3%
16:00 1875
 
0.9%
14:00 1598
 
0.8%
Other values (1666) 77701
36.7%
2025-03-22T16:33:34.498527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 339660
22.9%
2 179097
12.1%
1 172224
11.6%
141022
9.5%
/ 141022
9.5%
: 70511
 
4.8%
H 70511
 
4.8%
s 70511
 
4.8%
r 70511
 
4.8%
. 70511
 
4.8%
Other values (7) 155151
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1480731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 339660
22.9%
2 179097
12.1%
1 172224
11.6%
141022
9.5%
/ 141022
9.5%
: 70511
 
4.8%
H 70511
 
4.8%
s 70511
 
4.8%
r 70511
 
4.8%
. 70511
 
4.8%
Other values (7) 155151
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1480731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 339660
22.9%
2 179097
12.1%
1 172224
11.6%
141022
9.5%
/ 141022
9.5%
: 70511
 
4.8%
H 70511
 
4.8%
s 70511
 
4.8%
r 70511
 
4.8%
. 70511
 
4.8%
Other values (7) 155151
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1480731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 339660
22.9%
2 179097
12.1%
1 172224
11.6%
141022
9.5%
/ 141022
9.5%
: 70511
 
4.8%
H 70511
 
4.8%
s 70511
 
4.8%
r 70511
 
4.8%
. 70511
 
4.8%
Other values (7) 155151
10.5%

estado
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
Adjudicado
55121 
Dejado Sin Efecto
13688 
Desierto
 
1702

Length

Max length17
Median length10
Mean length11.310604
Min length8

Characters and Unicode

Total characters797522
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdjudicado
2nd rowDesierto
3rd rowAdjudicado
4th rowAdjudicado
5th rowAdjudicado

Common Values

ValueCountFrequency (%)
Adjudicado 55121
78.2%
Dejado Sin Efecto 13688
 
19.4%
Desierto 1702
 
2.4%

Length

2025-03-22T16:33:34.693055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:34.856409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
adjudicado 55121
56.3%
dejado 13688
 
14.0%
sin 13688
 
14.0%
efecto 13688
 
14.0%
desierto 1702
 
1.7%

Most occurring characters

ValueCountFrequency (%)
d 179051
22.5%
o 84199
10.6%
i 70511
 
8.8%
a 68809
 
8.6%
c 68809
 
8.6%
j 68809
 
8.6%
A 55121
 
6.9%
u 55121
 
6.9%
e 30780
 
3.9%
27376
 
3.4%
Other values (8) 88936
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 797522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 179051
22.5%
o 84199
10.6%
i 70511
 
8.8%
a 68809
 
8.6%
c 68809
 
8.6%
j 68809
 
8.6%
A 55121
 
6.9%
u 55121
 
6.9%
e 30780
 
3.9%
27376
 
3.4%
Other values (8) 88936
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 797522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 179051
22.5%
o 84199
10.6%
i 70511
 
8.8%
a 68809
 
8.6%
c 68809
 
8.6%
j 68809
 
8.6%
A 55121
 
6.9%
u 55121
 
6.9%
e 30780
 
3.9%
27376
 
3.4%
Other values (8) 88936
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 797522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 179051
22.5%
o 84199
10.6%
i 70511
 
8.8%
a 68809
 
8.6%
c 68809
 
8.6%
j 68809
 
8.6%
A 55121
 
6.9%
u 55121
 
6.9%
e 30780
 
3.9%
27376
 
3.4%
Other values (8) 88936
11.2%
Distinct482
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
2025-03-22T16:33:35.405091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length149
Median length84
Mean length44.93272
Min length13

Characters and Unicode

Total characters3168251
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row23/000 - Dirección General de Administración - SG
2nd row23/000 - Dirección General de Administración - SG
3rd row23/000 - Dirección General de Administración - SG
4th row23/000 - Dirección General de Administración - SG
5th row23/000 - Dirección General de Administración - SG
ValueCountFrequency (%)
105514
 
20.0%
de 47878
 
9.1%
dirección 20687
 
3.9%
general 17457
 
3.3%
y 15655
 
3.0%
administración 13987
 
2.6%
compras 12464
 
2.4%
contrataciones 10963
 
2.1%
dnv 8497
 
1.6%
departamento 5442
 
1.0%
Other values (1182) 269886
51.1%
2025-03-22T16:33:36.209973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
459413
 
14.5%
i 195752
 
6.2%
a 193026
 
6.1%
e 188648
 
6.0%
n 170058
 
5.4%
r 145197
 
4.6%
- 112870
 
3.6%
o 108245
 
3.4%
c 106743
 
3.4%
t 99714
 
3.1%
Other values (74) 1388585
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3168251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
459413
 
14.5%
i 195752
 
6.2%
a 193026
 
6.1%
e 188648
 
6.0%
n 170058
 
5.4%
r 145197
 
4.6%
- 112870
 
3.6%
o 108245
 
3.4%
c 106743
 
3.4%
t 99714
 
3.1%
Other values (74) 1388585
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3168251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
459413
 
14.5%
i 195752
 
6.2%
a 193026
 
6.1%
e 188648
 
6.0%
n 170058
 
5.4%
r 145197
 
4.6%
- 112870
 
3.6%
o 108245
 
3.4%
c 106743
 
3.4%
t 99714
 
3.1%
Other values (74) 1388585
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3168251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
459413
 
14.5%
i 195752
 
6.2%
a 193026
 
6.1%
e 188648
 
6.0%
n 170058
 
5.4%
r 145197
 
4.6%
- 112870
 
3.6%
o 108245
 
3.4%
c 106743
 
3.4%
t 99714
 
3.1%
Other values (74) 1388585
43.8%
Distinct144
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
2025-03-22T16:33:36.657249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length84
Mean length43.063763
Min length25

Characters and Unicode

Total characters3036469
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row301 - Secretaria General de la Presidencia de la Nación
2nd row301 - Secretaria General de la Presidencia de la Nación
3rd row301 - Secretaria General de la Presidencia de la Nación
4th row301 - Secretaria General de la Presidencia de la Nación
5th row301 - Secretaria General de la Presidencia de la Nación
ValueCountFrequency (%)
70562
 
13.7%
de 59571
 
11.6%
nacional 28441
 
5.5%
general 22598
 
4.4%
estado 21874
 
4.3%
mayor 21483
 
4.2%
la 16785
 
3.3%
del 11616
 
2.3%
dirección 9566
 
1.9%
ejercito 8904
 
1.7%
Other values (387) 242292
47.2%
2025-03-22T16:33:37.545087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
443554
14.6%
a 297080
 
9.8%
e 246763
 
8.1%
i 204111
 
6.7%
r 171301
 
5.6%
o 164535
 
5.4%
d 152608
 
5.0%
n 144387
 
4.8%
l 123452
 
4.1%
c 105878
 
3.5%
Other values (64) 982800
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3036469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
443554
14.6%
a 297080
 
9.8%
e 246763
 
8.1%
i 204111
 
6.7%
r 171301
 
5.6%
o 164535
 
5.4%
d 152608
 
5.0%
n 144387
 
4.8%
l 123452
 
4.1%
c 105878
 
3.5%
Other values (64) 982800
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3036469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
443554
14.6%
a 297080
 
9.8%
e 246763
 
8.1%
i 204111
 
6.7%
r 171301
 
5.6%
o 164535
 
5.4%
d 152608
 
5.0%
n 144387
 
4.8%
l 123452
 
4.1%
c 105878
 
3.5%
Other values (64) 982800
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3036469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
443554
14.6%
a 297080
 
9.8%
e 246763
 
8.1%
i 204111
 
6.7%
r 171301
 
5.6%
o 164535
 
5.4%
d 152608
 
5.0%
n 144387
 
4.8%
l 123452
 
4.1%
c 105878
 
3.5%
Other values (64) 982800
32.4%

num_expediente
Text

Missing 

Distinct66201
Distinct (%)95.0%
Missing825
Missing (%)1.2%
Memory size6.0 MiB
2025-03-22T16:33:38.279069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length41
Mean length33.60599
Min length29

Characters and Unicode

Total characters2341867
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62977 ?
Unique (%)90.4%

Sample

1st rowEX-2016-00697885- -APN-DPYS#SGP
2nd rowEX-2016-01031683- -APN-DPYS#SGP
3rd rowEX-2016-01358346- -APN-DDMYA#SGP
4th rowEX-2016-01392005- -APN-DDMYA#SGP
5th rowEX-2016-00474707- -APN-DPYS#SGP
ValueCountFrequency (%)
apn-dcon#faa 1991
 
1.4%
apn-dgit#ara 1423
 
1.0%
apn-dcyc#mc 1095
 
0.8%
apn-dacmysg#anlis 1004
 
0.7%
apn-dc#hp 946
 
0.7%
apn-dcyc#mds 827
 
0.6%
apn-gaen#cnea 800
 
0.6%
apn-da#inidep 791
 
0.6%
apn-dmza#dnv 787
 
0.6%
apn-gasnya#cnea 776
 
0.6%
Other values (66773) 128932
92.5%
2025-03-22T16:33:39.814043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

etapa
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size6.2 MiB
Única
69612 
Múltiple
 
45
Otros
 
29

Length

Max length8
Median length5
Mean length5.0019373
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica

Common Values

ValueCountFrequency (%)
Única 69612
98.7%
Múltiple 45
 
0.1%
Otros 29
 
< 0.1%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:40.081870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:40.248034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

modalidad
Categorical

High correlation  Imbalance  Missing 

Distinct15
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size4.8 MiB
Sin modalidad
58398 
Orden de compra abierta
10228 
Sin modalidad - Llave en mano
 
840
Selección Directa
 
107
Solicitud De Cotizaciones
 
27
Other values (10)
 
86

Length

Max length60
Median length13
Mean length14.690081
Min length13

Characters and Unicode

Total characters1023693
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSin modalidad
2nd rowSin modalidad
3rd rowOrden de compra abierta
4th rowSin modalidad
5th rowSin modalidad

Common Values

ValueCountFrequency (%)
Sin modalidad 58398
82.8%
Orden de compra abierta 10228
 
14.5%
Sin modalidad - Llave en mano 840
 
1.2%
Selección Directa 107
 
0.2%
Solicitud De Cotizaciones 27
 
< 0.1%
Solicitud De Ofertas 23
 
< 0.1%
Orden de compra abierta - Llave en mano 17
 
< 0.1%
Acuerdo Marco 15
 
< 0.1%
Sin modalidad - Precio máximo 9
 
< 0.1%
Sin modalidad - Compra Consolidada 8
 
< 0.1%
Other values (5) 14
 
< 0.1%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:40.421275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sin 59263
36.3%
modalidad 59263
36.3%
de 10302
 
6.3%
compra 10260
 
6.3%
orden 10247
 
6.3%
abierta 10247
 
6.3%
889
 
0.5%
llave 858
 
0.5%
en 858
 
0.5%
mano 858
 
0.5%
Other values (15) 406
 
0.2%

Most occurring characters

ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

moneda
Categorical

Imbalance  Missing 

Distinct17
Distinct (%)< 0.1%
Missing841
Missing (%)1.2%
Memory size4.8 MiB
Peso Argentino
65023 
Dolar Estadounidense
 
2150
Dolar Estadounidense Peso Argentino
 
1735
Dolar Estadounidense Euro - European Monetary Union Peso Argentino
 
483
Euro - European Monetary Union
 
141
Other values (12)
 
138

Length

Max length87
Median length14
Mean length15.168078
Min length4

Characters and Unicode

Total characters1056760
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPeso Argentino
2nd rowPeso Argentino
3rd rowPeso Argentino
4th rowPeso Argentino
5th rowPeso Argentino

Common Values

ValueCountFrequency (%)
Peso Argentino 65023
92.2%
Dolar Estadounidense 2150
 
3.0%
Dolar Estadounidense Peso Argentino 1735
 
2.5%
Dolar Estadounidense Euro - European Monetary Union Peso Argentino 483
 
0.7%
Euro - European Monetary Union 141
 
0.2%
Dolar Estadounidense Euro - European Monetary Union 100
 
0.1%
Euro - European Monetary Union Peso Argentino 20
 
< 0.1%
Libra Esterlina 6
 
< 0.1%
Dolar Estadounidense Real 2
 
< 0.1%
Real 2
 
< 0.1%
Other values (7) 8
 
< 0.1%
(Missing) 841
 
1.2%

Length

2025-03-22T16:33:40.617868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peso 67267
45.7%
argentino 67267
45.7%
dolar 4476
 
3.0%
estadounidense 4476
 
3.0%
euro 749
 
0.5%
749
 
0.5%
european 749
 
0.5%
monetary 749
 
0.5%
union 749
 
0.5%
libra 10
 
< 0.1%
Other values (2) 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 146492
13.9%
o 146482
13.9%
e 145004
13.7%
77591
7.3%
s 76229
7.2%
r 74010
7.0%
i 72512
6.9%
t 72502
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111404
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1056760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 146492
13.9%
o 146482
13.9%
e 145004
13.7%
77591
7.3%
s 76229
7.2%
r 74010
7.0%
i 72512
6.9%
t 72502
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111404
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1056760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 146492
13.9%
o 146482
13.9%
e 145004
13.7%
77591
7.3%
s 76229
7.2%
r 74010
7.0%
i 72512
6.9%
t 72502
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111404
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1056760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 146492
13.9%
o 146482
13.9%
e 145004
13.7%
77591
7.3%
s 76229
7.2%
r 74010
7.0%
i 72512
6.9%
t 72502
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111404
10.5%

encuadre_legal
Categorical

High correlation  Missing 

Distinct30
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size14.3 MiB
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14
31150 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.12
17560 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10
6243 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.12
3919 
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14
3321 
Other values (25)
7493 

Length

Max length144
Median length64
Mean length65.877264
Min length25

Characters and Unicode

Total characters4590723
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDecreto Delegado N° 1023/2001 Art. 25
2nd rowDecreto Delegado N° 1023/2001 Art. 25
3rd rowDecreto Delegado N° 1023/2001 Art. 25
4th rowDecreto Delegado N° 1023/2001 Art. 25
5th rowDecreto Delegado N° 1023/2001 Art. 25

Common Values

ValueCountFrequency (%)
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14 31150
44.2%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.12 17560
24.9%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10 6243
 
8.9%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.12 3919
 
5.6%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14 3321
 
4.7%
Decreto Delegado N° 1023/2001 Art. 25 2282
 
3.2%
Decreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.10 Decreto N°1030/2016 Art. 25 1892
 
2.7%
Decreto N°1030/2016 Art.12 783
 
1.1%
Decreto N°1030/2016 Art.14 506
 
0.7%
SUPERINTENDENCIA DE BIENESTAR Decreto 910/2018 415
 
0.6%
Other values (20) 1615
 
2.3%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:40.863650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
decreto 143019
22.1%
art 76967
11.9%
25 76505
11.8%
n°1030/2016 75332
11.6%
67147
10.4%
delegado 66878
10.3%
1023/2001 66878
10.3%
art.14 35114
 
5.4%
art.12 22340
 
3.5%
art.10 8251
 
1.3%
Other values (56) 8875
 
1.4%

Most occurring characters

ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

cotizacion
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size10.9 MiB
No admite cotización parcial por renglón
57499 
Se admite cotización parcial por renglón
12187 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters2787440
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo admite cotización parcial por renglón
2nd rowSe admite cotización parcial por renglón
3rd rowNo admite cotización parcial por renglón
4th rowNo admite cotización parcial por renglón
5th rowSe admite cotización parcial por renglón

Common Values

ValueCountFrequency (%)
No admite cotización parcial por renglón 57499
81.5%
Se admite cotización parcial por renglón 12187
 
17.3%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:41.065202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:41.219650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
admite 69686
16.7%
cotización 69686
16.7%
parcial 69686
16.7%
renglón 69686
16.7%
por 69686
16.7%
no 57499
13.8%
se 12187
 
2.9%

Most occurring characters

ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

tipo_doc_compra
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size4.8 MiB
Orden de compra
67341 
Contrato
 
1898
Orden de venta
 
432
Acuerdo
 
15

Length

Max length15
Median length15
Mean length14.801424
Min length7

Characters and Unicode

Total characters1031452
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrden de compra
2nd rowOrden de compra
3rd rowOrden de compra
4th rowOrden de compra
5th rowOrden de compra

Common Values

ValueCountFrequency (%)
Orden de compra 67341
95.5%
Contrato 1898
 
2.7%
Orden de venta 432
 
0.6%
Acuerdo 15
 
< 0.1%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:41.416956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:41.601085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
orden 67773
33.0%
de 67773
33.0%
compra 67341
32.8%
contrato 1898
 
0.9%
venta 432
 
0.2%
acuerdo 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

lugar_recepcion
Text

Missing 

Distinct8244
Distinct (%)11.9%
Missing1400
Missing (%)2.0%
Memory size9.6 MiB
2025-03-22T16:33:42.549618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length238
Mean length43.154346
Min length1

Characters and Unicode

Total characters2982440
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6314 ?
Unique (%)9.1%

Sample

1st row25 de Mayo 658, 4° piso.
2nd row25 de Mayo 658, 4° piso.
3rd row25 de Mayo 658, 4° piso.
4th row25 de Mayo 658, 4° piso.
5th row25 de Mayo 658, 4° piso.
ValueCountFrequency (%)
31108
 
5.6%
de 25265
 
4.6%
av 22886
 
4.2%
piso 19224
 
3.5%
caba 10440
 
1.9%
aires 8894
 
1.6%
buenos 8718
 
1.6%
y 8068
 
1.5%
7160
 
1.3%
oficina 6994
 
1.3%
Other values (4991) 402439
73.0%
2025-03-22T16:33:44.052272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
482220
 
16.2%
A 156301
 
5.2%
a 148353
 
5.0%
e 123429
 
4.1%
o 117199
 
3.9%
i 104133
 
3.5%
r 85189
 
2.9%
n 82625
 
2.8%
C 70445
 
2.4%
. 65486
 
2.2%
Other values (102) 1547060
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2982440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
482220
 
16.2%
A 156301
 
5.2%
a 148353
 
5.0%
e 123429
 
4.1%
o 117199
 
3.9%
i 104133
 
3.5%
r 85189
 
2.9%
n 82625
 
2.8%
C 70445
 
2.4%
. 65486
 
2.2%
Other values (102) 1547060
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2982440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
482220
 
16.2%
A 156301
 
5.2%
a 148353
 
5.0%
e 123429
 
4.1%
o 117199
 
3.9%
i 104133
 
3.5%
r 85189
 
2.9%
n 82625
 
2.8%
C 70445
 
2.4%
. 65486
 
2.2%
Other values (102) 1547060
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2982440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
482220
 
16.2%
A 156301
 
5.2%
a 148353
 
5.0%
e 123429
 
4.1%
o 117199
 
3.9%
i 104133
 
3.5%
r 85189
 
2.9%
n 82625
 
2.8%
C 70445
 
2.4%
. 65486
 
2.2%
Other values (102) 1547060
51.9%

plazo_oferta
Text

Missing 

Distinct69
Distinct (%)0.1%
Missing825
Missing (%)1.2%
Memory size9.9 MiB
2025-03-22T16:33:44.526981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length33
Mean length32.989137
Min length23

Characters and Unicode

Total characters2298881
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row60 Días corridos Acto de apertura
2nd row60 Días corridos Acto de apertura
3rd row60 Días corridos Acto de apertura
4th row60 Días corridos Perfeccionamiento del documento contractual
5th row60 Días corridos Acto de apertura
ValueCountFrequency (%)
de 69657
16.7%
días 69566
16.6%
apertura 69505
16.6%
acto 69505
16.6%
60 66064
15.8%
corridos 64534
15.4%
hábiles 5032
 
1.2%
90 1333
 
0.3%
30 935
 
0.2%
120 559
 
0.1%
Other values (40) 1563
 
0.4%
2025-03-22T16:33:45.216986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

requiere_pago
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size4.0 MiB
No
69575 
 
111

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69575
98.7%
111
 
0.2%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:45.436255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:45.657755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69575
99.8%
111
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

apartado
Categorical

High correlation  Imbalance  Missing 

Distinct23
Distinct (%)0.1%
Missing33215
Missing (%)47.1%
Memory size6.2 MiB
Apartado 1: Compulsa Abreviada Por Monto
25203 
Apartado 3: Adjudicación Simple por Exclusividad
3461 
Apartado 8: Adjudicación Simple Interadministrativa
3256 
Apartado 2: Adjudicación Simple por Especialidad
 
1170
Apartado 7: Adjudicación Simple por Desarme, Traslado o Examen Previo
 
1018
Other values (18)
3188 

Length

Max length84
Median length40
Mean length44.260162
Min length40

Characters and Unicode

Total characters1650727
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowApartado 1: Compulsa Abreviada Por Monto
2nd rowApartado 1: Compulsa Abreviada Por Monto
3rd rowApartado 5: Compulsa Abreviada por Urgencia
4th rowApartado 1: Compulsa Abreviada Por Monto
5th rowApartado 1: Compulsa Abreviada Por Monto

Common Values

ValueCountFrequency (%)
Apartado 1: Compulsa Abreviada Por Monto 25203
35.7%
Apartado 3: Adjudicación Simple por Exclusividad 3461
 
4.9%
Apartado 8: Adjudicación Simple Interadministrativa 3256
 
4.6%
Apartado 2: Adjudicación Simple por Especialidad 1170
 
1.7%
Apartado 7: Adjudicación Simple por Desarme, Traslado o Examen Previo 1018
 
1.4%
Apartado 5: Compulsa Abreviada por Urgencia 764
 
1.1%
Apartado 9: Adjudicación Simple con Universidades Nacionales 656
 
0.9%
Apartado 11: Adjudicación Simple por Locación de Inmuebles 620
 
0.9%
Apartado 10: Adjudicación Simple con Efectores de Desarrollo Local y Economía Social 495
 
0.7%
Apartado 5: Compulsa Abreviada por Emergencia 292
 
0.4%
Other values (13) 361
 
0.5%
(Missing) 33215
47.1%

Length

2025-03-22T16:33:45.824202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartado 37296
16.2%
por 32842
14.3%
abreviada 26422
11.5%
compulsa 26422
11.5%
monto 25302
11.0%
1 25205
10.9%
adjudicación 10853
 
4.7%
simple 10853
 
4.7%
exclusividad 3476
 
1.5%
3 3469
 
1.5%
Other values (50) 28292
12.3%

Most occurring characters

ValueCountFrequency (%)
193136
 
11.7%
a 190879
 
11.6%
o 156740
 
9.5%
r 109917
 
6.7%
d 101529
 
6.2%
i 83739
 
5.1%
p 83418
 
5.1%
A 74571
 
4.5%
t 73202
 
4.4%
e 53585
 
3.2%
Other values (41) 530011
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1650727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
193136
 
11.7%
a 190879
 
11.6%
o 156740
 
9.5%
r 109917
 
6.7%
d 101529
 
6.2%
i 83739
 
5.1%
p 83418
 
5.1%
A 74571
 
4.5%
t 73202
 
4.4%
e 53585
 
3.2%
Other values (41) 530011
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1650727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
193136
 
11.7%
a 190879
 
11.6%
o 156740
 
9.5%
r 109917
 
6.7%
d 101529
 
6.2%
i 83739
 
5.1%
p 83418
 
5.1%
A 74571
 
4.5%
t 73202
 
4.4%
e 53585
 
3.2%
Other values (41) 530011
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1650727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
193136
 
11.7%
a 190879
 
11.6%
o 156740
 
9.5%
r 109917
 
6.7%
d 101529
 
6.2%
i 83739
 
5.1%
p 83418
 
5.1%
A 74571
 
4.5%
t 73202
 
4.4%
e 53585
 
3.2%
Other values (41) 530011
32.1%

etapa_lic
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size6.2 MiB
Única
69612 
Múltiple
 
45
Otros
 
29

Length

Max length8
Median length5
Mean length5.0019373
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica

Common Values

ValueCountFrequency (%)
Única 69612
98.7%
Múltiple 45
 
0.1%
Otros 29
 
< 0.1%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:46.010949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:46.217968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

etapa_autorizacion_pliego
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing43649
Missing (%)61.9%
Memory size5.6 MiB
Autorización del pliego
26862 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters617826
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutorización del pliego
2nd rowAutorización del pliego
3rd rowAutorización del pliego
4th rowAutorización del pliego
5th rowAutorización del pliego

Common Values

ValueCountFrequency (%)
Autorización del pliego 26862
38.1%
(Missing) 43649
61.9%

Length

2025-03-22T16:33:46.382789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:46.521494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
autorización 26862
33.3%
del 26862
33.3%
pliego 26862
33.3%

Most occurring characters

ValueCountFrequency (%)
i 80586
13.0%
l 53724
 
8.7%
o 53724
 
8.7%
e 53724
 
8.7%
53724
 
8.7%
A 26862
 
4.3%
r 26862
 
4.3%
t 26862
 
4.3%
u 26862
 
4.3%
c 26862
 
4.3%
Other values (7) 188034
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 617826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 80586
13.0%
l 53724
 
8.7%
o 53724
 
8.7%
e 53724
 
8.7%
53724
 
8.7%
A 26862
 
4.3%
r 26862
 
4.3%
t 26862
 
4.3%
u 26862
 
4.3%
c 26862
 
4.3%
Other values (7) 188034
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 617826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 80586
13.0%
l 53724
 
8.7%
o 53724
 
8.7%
e 53724
 
8.7%
53724
 
8.7%
A 26862
 
4.3%
r 26862
 
4.3%
t 26862
 
4.3%
u 26862
 
4.3%
c 26862
 
4.3%
Other values (7) 188034
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 617826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 80586
13.0%
l 53724
 
8.7%
o 53724
 
8.7%
e 53724
 
8.7%
53724
 
8.7%
A 26862
 
4.3%
r 26862
 
4.3%
t 26862
 
4.3%
u 26862
 
4.3%
c 26862
 
4.3%
Other values (7) 188034
30.4%

etapa_autorizacion_llamado
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing43703
Missing (%)62.0%
Memory size5.6 MiB
Autorización de llamado
26808 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters616584
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutorización de llamado
2nd rowAutorización de llamado
3rd rowAutorización de llamado
4th rowAutorización de llamado
5th rowAutorización de llamado

Common Values

ValueCountFrequency (%)
Autorización de llamado 26808
38.0%
(Missing) 43703
62.0%

Length

2025-03-22T16:33:46.672125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:46.814308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
autorización 26808
33.3%
de 26808
33.3%
llamado 26808
33.3%

Most occurring characters

ValueCountFrequency (%)
a 80424
13.0%
o 53616
 
8.7%
i 53616
 
8.7%
53616
 
8.7%
d 53616
 
8.7%
l 53616
 
8.7%
t 26808
 
4.3%
A 26808
 
4.3%
z 26808
 
4.3%
r 26808
 
4.3%
Other values (6) 160848
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 616584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 80424
13.0%
o 53616
 
8.7%
i 53616
 
8.7%
53616
 
8.7%
d 53616
 
8.7%
l 53616
 
8.7%
t 26808
 
4.3%
A 26808
 
4.3%
z 26808
 
4.3%
r 26808
 
4.3%
Other values (6) 160848
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 616584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 80424
13.0%
o 53616
 
8.7%
i 53616
 
8.7%
53616
 
8.7%
d 53616
 
8.7%
l 53616
 
8.7%
t 26808
 
4.3%
A 26808
 
4.3%
z 26808
 
4.3%
r 26808
 
4.3%
Other values (6) 160848
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 616584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 80424
13.0%
o 53616
 
8.7%
i 53616
 
8.7%
53616
 
8.7%
d 53616
 
8.7%
l 53616
 
8.7%
t 26808
 
4.3%
A 26808
 
4.3%
z 26808
 
4.3%
r 26808
 
4.3%
Other values (6) 160848
26.1%

etapa_acto_apertura
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing34105
Missing (%)48.4%
Memory size4.4 MiB
Acto de apertura
36406 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters582496
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActo de apertura
2nd rowActo de apertura
3rd rowActo de apertura
4th rowActo de apertura
5th rowActo de apertura

Common Values

ValueCountFrequency (%)
Acto de apertura 36406
51.6%
(Missing) 34105
48.4%

Length

2025-03-22T16:33:46.971158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:47.123794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
acto 36406
33.3%
de 36406
33.3%
apertura 36406
33.3%

Most occurring characters

ValueCountFrequency (%)
t 72812
12.5%
e 72812
12.5%
72812
12.5%
r 72812
12.5%
a 72812
12.5%
c 36406
6.2%
A 36406
6.2%
d 36406
6.2%
o 36406
6.2%
p 36406
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 582496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 72812
12.5%
e 72812
12.5%
72812
12.5%
r 72812
12.5%
a 72812
12.5%
c 36406
6.2%
A 36406
6.2%
d 36406
6.2%
o 36406
6.2%
p 36406
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 582496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 72812
12.5%
e 72812
12.5%
72812
12.5%
r 72812
12.5%
a 72812
12.5%
c 36406
6.2%
A 36406
6.2%
d 36406
6.2%
o 36406
6.2%
p 36406
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 582496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 72812
12.5%
e 72812
12.5%
72812
12.5%
r 72812
12.5%
a 72812
12.5%
c 36406
6.2%
A 36406
6.2%
d 36406
6.2%
o 36406
6.2%
p 36406
6.2%

genera_recursos
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size4.0 MiB
No
69055 
Si
 
631

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69055
97.9%
Si 631
 
0.9%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:47.280075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:47.431821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69055
99.1%
si 631
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

financiamiento_externo
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing825
Missing (%)1.2%
Memory size4.0 MiB
No
69500 
Si
 
186

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 69500
98.6%
Si 186
 
0.3%
(Missing) 825
 
1.2%

Length

2025-03-22T16:33:47.596358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:47.793950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 69500
99.7%
si 186
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

acepta_prorroga
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1235
Missing (%)1.8%
Memory size4.5 MiB
No
50932 
18344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters138552
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 50932
72.2%
18344
 
26.0%
(Missing) 1235
 
1.8%

Length

2025-03-22T16:33:47.957738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:48.130138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 50932
73.5%
18344
 
26.5%

Most occurring characters

ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

tipo_de_bienes
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing69880
Missing (%)99.1%
Memory size3.8 MiB
Muebles
338 
Inmuebles
293 

Length

Max length9
Median length7
Mean length7.9286846
Min length7

Characters and Unicode

Total characters5003
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInmuebles
2nd rowInmuebles
3rd rowInmuebles
4th rowInmuebles
5th rowInmuebles

Common Values

ValueCountFrequency (%)
Muebles 338
 
0.5%
Inmuebles 293
 
0.4%
(Missing) 69880
99.1%

Length

2025-03-22T16:33:48.338289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:48.521874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
muebles 338
53.6%
inmuebles 293
46.4%

Most occurring characters

ValueCountFrequency (%)
e 1262
25.2%
s 631
12.6%
u 631
12.6%
b 631
12.6%
l 631
12.6%
M 338
 
6.8%
I 293
 
5.9%
n 293
 
5.9%
m 293
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1262
25.2%
s 631
12.6%
u 631
12.6%
b 631
12.6%
l 631
12.6%
M 338
 
6.8%
I 293
 
5.9%
n 293
 
5.9%
m 293
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1262
25.2%
s 631
12.6%
u 631
12.6%
b 631
12.6%
l 631
12.6%
M 338
 
6.8%
I 293
 
5.9%
n 293
 
5.9%
m 293
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1262
25.2%
s 631
12.6%
u 631
12.6%
b 631
12.6%
l 631
12.6%
M 338
 
6.8%
I 293
 
5.9%
n 293
 
5.9%
m 293
 
5.9%

genera_recursos_mediante
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.8%
Missing69880
Missing (%)99.1%
Memory size3.8 MiB
Venta
410 
Concesión
204 
Cesión
 
8
Alquiler
 
7
Indefinida
 
2

Length

Max length10
Median length5
Mean length6.3549921
Min length5

Characters and Unicode

Total characters4010
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConcesión
2nd rowConcesión
3rd rowConcesión
4th rowConcesión
5th rowConcesión

Common Values

ValueCountFrequency (%)
Venta 410
 
0.6%
Concesión 204
 
0.3%
Cesión 8
 
< 0.1%
Alquiler 7
 
< 0.1%
Indefinida 2
 
< 0.1%
(Missing) 69880
99.1%

Length

2025-03-22T16:33:48.687548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T16:33:48.875421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
venta 410
65.0%
concesión 204
32.3%
cesión 8
 
1.3%
alquiler 7
 
1.1%
indefinida 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 830
20.7%
e 631
15.7%
a 412
10.3%
V 410
10.2%
t 410
10.2%
i 223
 
5.6%
C 212
 
5.3%
ó 212
 
5.3%
s 212
 
5.3%
o 204
 
5.1%
Other values (9) 254
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 830
20.7%
e 631
15.7%
a 412
10.3%
V 410
10.2%
t 410
10.2%
i 223
 
5.6%
C 212
 
5.3%
ó 212
 
5.3%
s 212
 
5.3%
o 204
 
5.1%
Other values (9) 254
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 830
20.7%
e 631
15.7%
a 412
10.3%
V 410
10.2%
t 410
10.2%
i 223
 
5.6%
C 212
 
5.3%
ó 212
 
5.3%
s 212
 
5.3%
o 204
 
5.1%
Other values (9) 254
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 830
20.7%
e 631
15.7%
a 412
10.3%
V 410
10.2%
t 410
10.2%
i 223
 
5.6%
C 212
 
5.3%
ó 212
 
5.3%
s 212
 
5.3%
o 204
 
5.1%
Other values (9) 254
 
6.3%

cro_fecha_publicacion
Text

Missing 

Distinct25991
Distinct (%)37.3%
Missing898
Missing (%)1.3%
Memory size5.2 MiB
2025-03-22T16:33:49.381473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1461873
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13788 ?
Unique (%)19.8%

Sample

1st row21/09/2016 18:01 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row07/12/2016 18:30 Hrs.
4th row15/11/2016 16:30 Hrs.
5th row21/09/2016 15:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
08:00 13835
 
6.6%
10:00 8170
 
3.9%
09:00 6357
 
3.0%
12:00 4536
 
2.2%
14:00 3518
 
1.7%
15:00 3119
 
1.5%
13:00 3043
 
1.5%
11:00 2776
 
1.3%
16:00 2401
 
1.1%
Other values (2006) 91471
43.8%
2025-03-22T16:33:50.077569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 182251
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 182251
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 182251
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 182251
12.5%
Distinct31332
Distinct (%)45.0%
Missing898
Missing (%)1.3%
Memory size5.2 MiB
2025-03-22T16:33:50.533384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1461873
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19336 ?
Unique (%)27.8%

Sample

1st row21/09/2016 18:02 Hrs.
2nd row02/09/2016 10:00 Hrs.
3rd row07/12/2016 19:00 Hrs.
4th row15/11/2016 16:31 Hrs.
5th row21/09/2016 15:01 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 9076
 
4.3%
08:00 8662
 
4.1%
09:00 8095
 
3.9%
11:00 4286
 
2.1%
12:00 2796
 
1.3%
08:30 1837
 
0.9%
08:01 1819
 
0.9%
13:00 1811
 
0.9%
15:00 1774
 
0.8%
Other values (2099) 99070
47.4%
2025-03-22T16:33:51.612174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 185268
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 185268
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 185268
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 185268
12.7%
Distinct19809
Distinct (%)28.5%
Missing898
Missing (%)1.3%
Memory size5.2 MiB
2025-03-22T16:33:52.227324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1461873
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7106 ?
Unique (%)10.2%

Sample

1st row06/10/2016 13:01 Hrs.
2nd row07/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row24/11/2016 16:31 Hrs.
5th row21/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
12:00 11635
 
5.6%
10:00 10560
 
5.1%
13:00 6186
 
3.0%
16:00 6012
 
2.9%
09:00 4407
 
2.1%
11:00 4370
 
2.1%
17:00 4140
 
2.0%
15:00 3592
 
1.7%
18:00 3590
 
1.7%
Other values (1865) 84734
40.6%
2025-03-22T16:33:53.344621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 171988
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 171988
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 171988
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 171988
11.8%

cro_cant_dias_publicar
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)0.2%
Missing61808
Missing (%)87.7%
Infinite0
Infinite (%)0.0%
Mean2.0304493
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size551.0 KiB
2025-03-22T16:33:53.547014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75015749
Coefficient of variation (CV)0.36945394
Kurtosis490.54092
Mean2.0304493
Median Absolute Deviation (MAD)0
Skewness19.063048
Sum17671
Variance0.56273626
MonotonicityNot monotonic
2025-03-22T16:33:53.706968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 8401
 
11.9%
1 211
 
0.3%
3 24
 
< 0.1%
5 22
 
< 0.1%
10 16
 
< 0.1%
8 9
 
< 0.1%
7 8
 
< 0.1%
6 2
 
< 0.1%
20 2
 
< 0.1%
13 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
(Missing) 61808
87.7%
ValueCountFrequency (%)
1 211
 
0.3%
2 8401
11.9%
3 24
 
< 0.1%
4 1
 
< 0.1%
5 22
 
< 0.1%
6 2
 
< 0.1%
7 8
 
< 0.1%
8 9
 
< 0.1%
10 16
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 2
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
10 16
< 0.1%
8 9
< 0.1%
Distinct13986
Distinct (%)46.5%
Missing40417
Missing (%)57.3%
Memory size3.5 MiB
2025-03-22T16:33:54.442302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters631974
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8245 ?
Unique (%)27.4%

Sample

1st row27/09/2016 18:02 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row12/12/2016 10:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row26/09/2016 15:01 Hrs.
ValueCountFrequency (%)
hrs 30094
33.3%
10:00 5724
 
6.3%
08:00 5569
 
6.2%
09:00 3753
 
4.2%
11:00 2574
 
2.9%
12:00 1735
 
1.9%
07:00 942
 
1.0%
08:30 744
 
0.8%
13:00 631
 
0.7%
08:01 570
 
0.6%
Other values (1891) 37946
42.0%
2025-03-22T16:33:55.226280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 143624
22.7%
2 72808
11.5%
1 67634
10.7%
60188
9.5%
/ 60188
9.5%
: 30094
 
4.8%
H 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 77062
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143624
22.7%
2 72808
11.5%
1 67634
10.7%
60188
9.5%
/ 60188
9.5%
: 30094
 
4.8%
H 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 77062
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143624
22.7%
2 72808
11.5%
1 67634
10.7%
60188
9.5%
/ 60188
9.5%
: 30094
 
4.8%
H 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 77062
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143624
22.7%
2 72808
11.5%
1 67634
10.7%
60188
9.5%
/ 60188
9.5%
: 30094
 
4.8%
H 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 77062
12.2%
Distinct13762
Distinct (%)45.7%
Missing40417
Missing (%)57.3%
Memory size3.5 MiB
2025-03-22T16:33:55.716902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters631974
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7541 ?
Unique (%)25.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row27/10/2016 11:59 Hrs.
ValueCountFrequency (%)
hrs 30094
33.3%
12:00 5482
 
6.1%
10:00 4924
 
5.5%
11:00 4252
 
4.7%
09:00 2882
 
3.2%
08:00 1645
 
1.8%
13:00 1519
 
1.7%
16:00 1012
 
1.1%
10:30 908
 
1.0%
15:00 877
 
1.0%
Other values (1694) 36687
40.6%
2025-03-22T16:33:56.803044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 136427
21.6%
2 76910
12.2%
1 73680
11.7%
/ 60188
9.5%
60188
9.5%
H 30094
 
4.8%
: 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 74111
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 136427
21.6%
2 76910
12.2%
1 73680
11.7%
/ 60188
9.5%
60188
9.5%
H 30094
 
4.8%
: 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 74111
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 136427
21.6%
2 76910
12.2%
1 73680
11.7%
/ 60188
9.5%
60188
9.5%
H 30094
 
4.8%
: 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 74111
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 631974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 136427
21.6%
2 76910
12.2%
1 73680
11.7%
/ 60188
9.5%
60188
9.5%
H 30094
 
4.8%
: 30094
 
4.8%
s 30094
 
4.8%
r 30094
 
4.8%
. 30094
 
4.8%
Other values (7) 74111
11.7%
Distinct15391
Distinct (%)22.1%
Missing898
Missing (%)1.3%
Memory size5.2 MiB
2025-03-22T16:33:57.413551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1461873
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5610 ?
Unique (%)8.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 18670
 
8.9%
12:00 11014
 
5.3%
11:00 10436
 
5.0%
09:00 8586
 
4.1%
08:00 3906
 
1.9%
13:00 3804
 
1.8%
15:00 2637
 
1.3%
16:00 1855
 
0.9%
14:00 1582
 
0.8%
Other values (1666) 76736
36.7%
2025-03-22T16:33:58.277316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.1%
1 170475
11.7%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 153450
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.1%
1 170475
11.7%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 153450
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.1%
1 170475
11.7%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 153450
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.1%
1 170475
11.7%
139226
9.5%
/ 139226
9.5%
: 69613
 
4.8%
H 69613
 
4.8%
s 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
Other values (7) 153450
10.5%

inicio_contrato
Text

Missing 

Distinct229
Distinct (%)0.3%
Missing843
Missing (%)1.2%
Memory size10.3 MiB
2025-03-22T16:33:58.561085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length88
Median length56
Mean length62.578774
Min length56

Characters and Unicode

Total characters4359738
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.1%

Sample

1st rowA partir del perfeccionamiento del documento contractual
2nd rowDentro de los 20 Días corridos del perfeccionamiento del documento contractual
3rd rowA partir del perfeccionamiento del documento contractual
4th rowA partir del perfeccionamiento del documento contractual
5th rowA partir del perfeccionamiento del documento contractual
ValueCountFrequency (%)
del 140364
24.2%
perfeccionamiento 69668
12.0%
documento 69668
12.0%
contractual 69668
12.0%
a 54447
 
9.4%
partir 45130
 
7.8%
los 24538
 
4.2%
días 24538
 
4.2%
de 15221
 
2.6%
dentro 15221
 
2.6%
Other values (108) 51132
 
8.8%
2025-03-22T16:33:58.995441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
509927
11.7%
e 467468
10.7%
o 407363
9.3%
c 357972
 
8.2%
t 340051
 
7.8%
n 294921
 
6.8%
a 278672
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234885
 
5.4%
Other values (23) 954922
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4359738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
509927
11.7%
e 467468
10.7%
o 407363
9.3%
c 357972
 
8.2%
t 340051
 
7.8%
n 294921
 
6.8%
a 278672
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234885
 
5.4%
Other values (23) 954922
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4359738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
509927
11.7%
e 467468
10.7%
o 407363
9.3%
c 357972
 
8.2%
t 340051
 
7.8%
n 294921
 
6.8%
a 278672
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234885
 
5.4%
Other values (23) 954922
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4359738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
509927
11.7%
e 467468
10.7%
o 407363
9.3%
c 357972
 
8.2%
t 340051
 
7.8%
n 294921
 
6.8%
a 278672
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234885
 
5.4%
Other values (23) 954922
21.9%

duracion_contrato
Text

Missing 

Distinct327
Distinct (%)0.5%
Missing1235
Missing (%)1.8%
Memory size6.2 MiB
2025-03-22T16:33:59.303948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.03704
Min length5

Characters and Unicode

Total characters833878
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.1%

Sample

1st row5 Días hábiles
2nd row30 Días corridos
3rd row12 Meses
4th row12 Meses
5th row120 Días corridos
ValueCountFrequency (%)
días 38418
21.7%
meses 29590
16.7%
corridos 19659
11.1%
hábiles 18759
10.6%
12 18310
10.3%
15 9256
 
5.2%
30 7963
 
4.5%
6 4404
 
2.5%
10 4089
 
2.3%
60 3661
 
2.1%
Other values (193) 23229
13.1%
2025-03-22T16:33:59.954152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 136609
16.4%
108062
13.0%
e 78331
 
9.4%
o 40102
 
4.8%
r 39594
 
4.7%
í 38786
 
4.7%
a 38786
 
4.7%
D 38786
 
4.7%
i 38786
 
4.7%
1 35762
 
4.3%
Other values (18) 240274
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 833878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 136609
16.4%
108062
13.0%
e 78331
 
9.4%
o 40102
 
4.8%
r 39594
 
4.7%
í 38786
 
4.7%
a 38786
 
4.7%
D 38786
 
4.7%
i 38786
 
4.7%
1 35762
 
4.3%
Other values (18) 240274
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 833878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 136609
16.4%
108062
13.0%
e 78331
 
9.4%
o 40102
 
4.8%
r 39594
 
4.7%
í 38786
 
4.7%
a 38786
 
4.7%
D 38786
 
4.7%
i 38786
 
4.7%
1 35762
 
4.3%
Other values (18) 240274
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 833878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 136609
16.4%
108062
13.0%
e 78331
 
9.4%
o 40102
 
4.8%
r 39594
 
4.7%
í 38786
 
4.7%
a 38786
 
4.7%
D 38786
 
4.7%
i 38786
 
4.7%
1 35762
 
4.3%
Other values (18) 240274
28.8%

proveedores_participantes
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct63
Distinct (%)0.1%
Missing1196
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean4.9596624
Minimum0
Maximum319
Zeros2982
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size551.0 KiB
2025-03-22T16:34:00.226050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum319
Range319
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2393049
Coefficient of variation (CV)1.0563834
Kurtosis201.07912
Mean4.9596624
Median Absolute Deviation (MAD)2
Skewness5.4698742
Sum343779
Variance27.450316
MonotonicityNot monotonic
2025-03-22T16:34:00.443256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17559
24.9%
2 8052
11.4%
3 6995
 
9.9%
4 5894
 
8.4%
5 4993
 
7.1%
6 4245
 
6.0%
7 3391
 
4.8%
0 2982
 
4.2%
8 2845
 
4.0%
9 2308
 
3.3%
Other values (53) 10051
14.3%
ValueCountFrequency (%)
0 2982
 
4.2%
1 17559
24.9%
2 8052
11.4%
3 6995
 
9.9%
4 5894
 
8.4%
5 4993
 
7.1%
6 4245
 
6.0%
7 3391
 
4.8%
8 2845
 
4.0%
9 2308
 
3.3%
ValueCountFrequency (%)
319 1
< 0.1%
139 1
< 0.1%
99 1
< 0.1%
74 1
< 0.1%
65 2
< 0.1%
64 1
< 0.1%
60 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%

ofertas_confirmadas
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct44
Distinct (%)0.1%
Missing1196
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3.3495059
Minimum0
Maximum105
Zeros5839
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size551.0 KiB
2025-03-22T16:34:00.673086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile10
Maximum105
Range105
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5514448
Coefficient of variation (CV)1.0602892
Kurtosis21.331589
Mean3.3495059
Median Absolute Deviation (MAD)1
Skewness2.7758374
Sum232171
Variance12.61276
MonotonicityNot monotonic
2025-03-22T16:34:00.885766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 22248
31.6%
2 10032
14.2%
3 7694
 
10.9%
0 5839
 
8.3%
4 5764
 
8.2%
5 4434
 
6.3%
6 3336
 
4.7%
7 2540
 
3.6%
8 1833
 
2.6%
9 1312
 
1.9%
Other values (34) 4283
 
6.1%
(Missing) 1196
 
1.7%
ValueCountFrequency (%)
0 5839
 
8.3%
1 22248
31.6%
2 10032
14.2%
3 7694
 
10.9%
4 5764
 
8.2%
5 4434
 
6.3%
6 3336
 
4.7%
7 2540
 
3.6%
8 1833
 
2.6%
9 1312
 
1.9%
ValueCountFrequency (%)
105 1
 
< 0.1%
78 1
 
< 0.1%
53 1
 
< 0.1%
45 1
 
< 0.1%
42 3
< 0.1%
38 3
< 0.1%
37 1
 
< 0.1%
36 3
< 0.1%
35 4
< 0.1%
34 3
< 0.1%

Interactions

2025-03-22T16:33:24.055017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:22.940004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:23.515906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:24.267526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:23.136193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:23.700839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:24.467734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:23.341184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-22T16:33:23.875343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-22T16:34:01.058012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
acepta_prorrogaapartadocotizacioncro_cant_dias_publicarencuadre_legalestadoetapaetapa_licfinanciamiento_externogenera_recursosgenera_recursos_mediantemodalidadmonedaofertas_confirmadasproveedores_participantesrequiere_pagotipo_de_bienestipo_doc_compratipo_proceso
acepta_prorroga1.0000.2470.0530.0000.1270.0140.0110.0110.0000.0240.1550.1140.0490.0670.0170.0220.0800.0780.075
apartado0.2471.0000.1290.0000.3990.1440.0000.0001.0000.0490.5690.0540.0770.1010.0920.0000.4990.2551.000
cotizacion0.0530.1291.0000.0310.0780.0000.0080.0080.0190.0390.2380.0520.0120.0320.0000.0150.0550.0480.063
cro_cant_dias_publicar0.0000.0000.0311.0000.0550.0000.0000.0000.0000.0001.0000.0000.0000.007-0.0180.0001.0000.0000.081
encuadre_legal0.1270.3990.0780.0551.0000.0770.0550.0551.0000.7940.4810.3910.0670.0530.0510.0510.9050.5550.808
estado0.0140.1440.0000.0000.0771.0000.0080.0080.0050.0440.1020.0290.0200.0530.0100.0000.1260.0450.063
etapa0.0110.0000.0080.0000.0550.0081.0001.0000.0000.0001.0000.0090.0000.0000.0000.0001.0000.0000.122
etapa_lic0.0110.0000.0080.0000.0550.0081.0001.0000.0000.0001.0000.0090.0000.0000.0000.0001.0000.0000.122
financiamiento_externo0.0001.0000.0190.0001.0000.0050.0000.0001.0000.0001.0000.9180.0540.0000.0030.0001.0000.0090.405
genera_recursos0.0240.0490.0390.0000.7940.0440.0000.0000.0001.0001.0000.0660.0590.0700.0740.0001.0000.8460.791
genera_recursos_mediante0.1550.5690.2381.0000.4810.1021.0001.0001.0001.0001.0000.0180.3160.0000.0000.0000.7500.8890.508
modalidad0.1140.0540.0520.0000.3910.0290.0090.0090.9180.0660.0181.0000.0610.0100.0000.0100.0650.5800.101
moneda0.0490.0770.0120.0000.0670.0200.0000.0000.0540.0590.3160.0611.0000.0000.0000.0000.3230.0400.054
ofertas_confirmadas0.0670.1010.0320.0070.0530.0530.0000.0000.0000.0700.0000.0100.0001.0000.9020.0000.0000.0540.054
proveedores_participantes0.0170.0920.000-0.0180.0510.0100.0000.0000.0030.0740.0000.0000.0000.9021.0000.0000.0500.0520.050
requiere_pago0.0220.0000.0150.0000.0510.0000.0000.0000.0000.0000.0000.0100.0000.0000.0001.0000.0000.0000.042
tipo_de_bienes0.0800.4990.0551.0000.9050.1261.0001.0001.0001.0000.7500.0650.3230.0000.0500.0001.0000.6700.720
tipo_doc_compra0.0780.2550.0480.0000.5550.0450.0000.0000.0090.8460.8890.5800.0400.0540.0520.0000.6701.0000.553
tipo_proceso0.0751.0000.0630.0810.8080.0630.1220.1220.4050.7910.5080.1010.0540.0540.0500.0420.7200.5531.000

Missing values

2025-03-22T16:33:24.960900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-22T16:33:26.098201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-22T16:33:27.766060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

num_procesoExpedientenombre_procesotipo_procesofecha_aperturaestadounidad_ejecutoraservicio_administrativo_financieronum_expedienteetapamodalidadmonedaencuadre_legalcotizaciontipo_doc_compralugar_recepcionplazo_ofertarequiere_pagoapartadoetapa_licetapa_autorizacion_pliegoetapa_autorizacion_llamadoetapa_acto_aperturagenera_recursosfinanciamiento_externoacepta_prorrogatipo_de_bienesgenera_recursos_mediantecro_fecha_publicacioncro_fecha_inicio_consultascro_fecha_final_consultascro_cant_dias_publicarcro_fecha_inicio_recepcion_documentoscro_fecha_fin_recepcion_documentoscro_fecha_acto_aperturainicio_contratoduracion_contratoproveedores_participantesofertas_confirmadas
023-0009-LPR16EX-2016-00697885- -APN-DPYS#SGPAdquisición de elementos de electricidadLicitación Privada13/10/2016 13:02 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00697885- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN21/09/2016 18:01 Hrs.21/09/2016 18:02 Hrs.06/10/2016 13:01 Hrs.2.027/09/2016 18:02 Hrs.13/10/2016 13:02 Hrs.13/10/2016 13:02 Hrs.A partir del perfeccionamiento del documento contractual5 Días hábiles9.05.0
123-0010-LPR16EX-2016-01031683- -APN-DPYS#SGPAdquisición de elementos de plomería y cerrajería.Licitación Privada13/09/2016 11:00 Hrs.Desierto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-01031683- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN02/09/2016 09:00 Hrs.02/09/2016 10:00 Hrs.07/09/2016 11:00 Hrs.1.002/09/2016 09:00 Hrs.13/09/2016 11:00 Hrs.13/09/2016 11:00 Hrs.Dentro de los 20 Días corridos del perfeccionamiento del documento contractual30 Días corridos2.00.0
223-0011-LPR16EX-2016-01358346- -APN-DDMYA#SGPADQUISICIÓN INSUMOS PARA BAÑOSLicitación Privada19/12/2016 12:30 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-01358346- -APN-DDMYA#SGPÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN07/12/2016 18:30 Hrs.07/12/2016 19:00 Hrs.19/12/2016 12:00 Hrs.NaN12/12/2016 10:00 Hrs.19/12/2016 12:00 Hrs.19/12/2016 12:30 Hrs.A partir del perfeccionamiento del documento contractual12 Meses8.03.0
323-0012-LPR16EX-2016-01392005- -APN-DDMYA#SGPServicio anual de mantenimiento, y controles mensuales de Extintores, y adquisiciónLicitación Privada30/11/2016 16:31 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-01392005- -APN-DDMYA#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Perfeccionamiento del documento contractualNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN15/11/2016 16:30 Hrs.15/11/2016 16:31 Hrs.24/11/2016 16:31 Hrs.2.030/11/2016 16:30 Hrs.30/11/2016 16:30 Hrs.30/11/2016 16:31 Hrs.A partir del perfeccionamiento del documento contractual12 Meses6.05.0
423-0014-LPU16EX-2016-00474707- -APN-DPYS#SGPAdquisición de indumentaria.Licitación Pública27/10/2016 12:00 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00474707- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN21/09/2016 15:00 Hrs.21/09/2016 15:01 Hrs.21/10/2016 12:00 Hrs.2.026/09/2016 15:01 Hrs.27/10/2016 11:59 Hrs.27/10/2016 12:00 Hrs.A partir del perfeccionamiento del documento contractual120 Días corridos7.06.0
523-0015-LPU16EX-2016-00549712- -APN-DPYS#SGPServicio de transporte aéreo para el Sr. Presidente y ComitivaLicitación Pública09/09/2016 12:01 Hrs.Dejado Sin Efecto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00549712- -APN-DPYS#SGPÚnicaOrden de compra abiertaDolar EstadounidenseDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN09/08/2016 10:00 Hrs.09/08/2016 10:01 Hrs.06/09/2016 12:01 Hrs.2.009/08/2016 10:00 Hrs.09/09/2016 12:01 Hrs.09/09/2016 12:01 Hrs.A partir del perfeccionamiento del documento contractual12 Meses3.01.0
623-0017-LPU16EX-2016-00563975- -APN-DPYS#SGPServicio de mantenimiento integral, correctivo y preventivo de espacios verdes para la RPO.Licitación Pública14/11/2016 11:00 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00563975- -APN-DPYS#SGPÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días hábiles Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN05/09/2016 13:00 Hrs.05/09/2016 13:30 Hrs.08/11/2016 10:00 Hrs.2.019/10/2016 14:00 Hrs.14/11/2016 11:00 Hrs.14/11/2016 11:00 Hrs.A partir del perfeccionamiento del documento contractual12 Meses13.08.0
723-0018-LPU16EX-2016-00669730- -APN-DPYS#SGPServicio de Limpieza Integral destinado a distintos edificios dependientes de la SECRETARIA GENERAL.Licitación Pública07/11/2016 12:00 Hrs.Dejado Sin Efecto23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00669730- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNaNNaN04/10/2016 13:00 Hrs.06/10/2016 13:00 Hrs.01/11/2016 12:00 Hrs.2.006/10/2016 09:00 Hrs.07/11/2016 12:00 Hrs.07/11/2016 12:00 Hrs.A partir del perfeccionamiento del documento contractual12 Meses11.06.0
823-0019-LPU16EX-2016-00670612- -APN-DPYS#SGPServicio de mantenimiento de Aires Acondicionados y otros equipos para dependencias de la SEC. GRAL.Licitación Pública13/10/2016 11:05 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00670612- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN02/09/2016 08:00 Hrs.02/09/2016 08:01 Hrs.07/10/2016 17:00 Hrs.2.014/09/2016 08:01 Hrs.13/10/2016 11:05 Hrs.13/10/2016 11:05 Hrs.A partir del perfeccionamiento del documento contractual12 Meses8.04.0
923-0020-LPU16EX-2016-00801371- -APN-DPYS#SGPProvisión e Instalación de un Sistema de Iluminación Exterior del Edificio de Casa de GobiernoLicitación Pública21/10/2016 11:05 Hrs.Adjudicado23/000 - Dirección General de Administración - SG301 - Secretaria General de la Presidencia de la NaciónEX-2016-00801371- -APN-DPYS#SGPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25No admite cotización parcial por renglónOrden de compra25 de Mayo 658, 4° piso.60 Días corridos Acto de aperturaNoNaNÚnicaNaNNaNNaNNoNoNoNaNNaN19/09/2016 12:00 Hrs.19/09/2016 12:05 Hrs.18/10/2016 11:05 Hrs.2.022/09/2016 12:00 Hrs.21/10/2016 11:05 Hrs.21/10/2016 11:05 Hrs.A partir del perfeccionamiento del documento contractual90 Días corridos8.02.0
num_procesoExpedientenombre_procesotipo_procesofecha_aperturaestadounidad_ejecutoraservicio_administrativo_financieronum_expedienteetapamodalidadmonedaencuadre_legalcotizaciontipo_doc_compralugar_recepcionplazo_ofertarequiere_pagoapartadoetapa_licetapa_autorizacion_pliegoetapa_autorizacion_llamadoetapa_acto_aperturagenera_recursosfinanciamiento_externoacepta_prorrogatipo_de_bienesgenera_recursos_mediantecro_fecha_publicacioncro_fecha_inicio_consultascro_fecha_final_consultascro_cant_dias_publicarcro_fecha_inicio_recepcion_documentoscro_fecha_fin_recepcion_documentoscro_fecha_acto_aperturainicio_contratoduracion_contratoproveedores_participantesofertas_confirmadas
7050195-0021-CDI22EX-2022-33725983- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA EL RELEVAMIENTO DE POLITICAS DEL MINIST.Contratación Directa20/04/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2022-33725983- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraNaN90 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN12/04/2022 15:00 Hrs.12/04/2022 15:01 Hrs.18/04/2022 10:00 Hrs.NaNNaNNaN20/04/2022 10:00 Hrs.Dentro de los 45 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.0
7050295-0038-CDI22EX-2022-67241200- -APN-DCYC#MDSservicio de provisión de combustibles y lubricantesContratación Directa17/08/2022 08:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2022-67241200- -APN-DCYC#MDSÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de compraNaN60 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN09/08/2022 20:00 Hrs.09/08/2022 20:01 Hrs.16/08/2022 08:00 Hrs.NaNNaNNaN17/08/2022 08:00 Hrs.Dentro de los 5 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.0
7050395-0049-CDI22EX-2022-82329158- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA REALIZAR UNA ACTUALIZACIÓN CARTOGRÁFICAContratación Directa06/09/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2022-82329158- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraNaN60 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN26/08/2022 18:30 Hrs.26/08/2022 18:31 Hrs.31/08/2022 10:00 Hrs.NaNNaNNaN06/09/2022 10:00 Hrs.Dentro de los 5 Días hábiles del perfeccionamiento del documento contractual12 Meses0.00.0
7050495-0065-CDI22EX-2022-103379128- -APN-DCYC#MDSservicio de provisión de combustibles y lubricantesContratación Directa11/10/2022 09:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2022-103379128- -APN-DCYC#MDSÚnicaOrden de compra abiertaPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraNaN60 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN04/10/2022 11:00 Hrs.04/10/2022 11:01 Hrs.06/10/2022 09:00 Hrs.NaNNaNNaN11/10/2022 09:00 Hrs.Dentro de los 5 Días corridos del perfeccionamiento del documento contractual12 Meses0.00.0
7050595-0069-CDI22EX-2022-105655311- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NAC. PARA LA REALIZACIÓN DE UNA DIPLOMATURAContratación Directa31/10/2022 09:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2022-105655311- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraNaN60 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN24/10/2022 19:40 Hrs.24/10/2022 19:41 Hrs.27/10/2022 09:00 Hrs.NaNNaNNaN31/10/2022 09:00 Hrs.A partir del perfeccionamiento del documento contractual10 Meses0.00.0
7050695-0189-CDI21EX-2021-124397120- -APN-DCYC#MDSCONTRATACIÓN DIRECTA CON UNA UNIVERSIDAD NACIONAL PARA EL RELEVAMIENTO DE POLITICAS SOCIALESContratación Directa18/01/2022 10:00 Hrs.Desierto95/000 - Dirección General de Administración - MDS311 - Ministerio de Desarrollo SocialEX-2021-124397120- -APN-DCYC#MDSÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraNaN90 Días corridos Acto de aperturaNoApartado 9: Adjudicación Simple con Universidades NacionalesÚnicaNaNNaNActo de aperturaNoNoNoNaNNaN11/01/2022 20:00 Hrs.11/01/2022 20:01 Hrs.14/01/2022 10:00 Hrs.NaNNaNNaN18/01/2022 10:00 Hrs.Dentro de los 10 Días corridos del perfeccionamiento del documento contractual7 Meses0.00.0
7050796-0054-CDI22EX-2022-44338308- -APN-DC#HPADQUISICIÓN DE ÁCIDO 2,3 - DIMERCAPTOSUCCÍNICO PARA EL SERVICIO DE TOXICOLOGÍA.Contratación Directa21/06/2022 13:00 Hrs.Desierto96 - Dirección General de Administración908 - Hospital Nacional Profesor Alejandro PosadasEX-2022-44338308- -APN-DC#HPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de compraPTE. ILLIA y MARCONI C.P. (1684) PALOMAR60 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNoNaNNaN13/06/2022 08:00 Hrs.13/06/2022 09:00 Hrs.14/06/2022 13:00 Hrs.NaNNaNNaN21/06/2022 13:00 Hrs.A partir del perfeccionamiento del documento contractual6 Meses0.00.0
7050896-0118-CDI22EX-2022-121335498- -APN-DC#HPCALIBRACIÓN DE EQUIPAMIENTO Y ELEMENTOS DE MEDICIÓNContratación Directa23/11/2022 13:00 Hrs.Desierto96 - Dirección General de Administración908 - Hospital Nacional Profesor Alejandro PosadasEX-2022-121335498- -APN-DC#HPÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14Se admite cotización parcial por renglónOrden de compraPTE. ILLIA y MARCONI C.P. (1684) PALOMAR60 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNoNaNNaN17/11/2022 08:00 Hrs.17/11/2022 09:00 Hrs.22/11/2022 13:00 Hrs.NaNNaNNaN23/11/2022 13:00 Hrs.A partir del perfeccionamiento del documento contractual10 Días hábiles0.00.0
7050998-0012-CDI22EX-2022-44810023- -APN-DA#FMLCAVADQUISICIÓN DE VACUNAS ANTIGRIPALESContratación Directa16/05/2022 11:00 Hrs.Desierto98/00 - Dir. Compras101 - Fundación Miguel LilloEX-2022-44810023- -APN-DA#FMLCAVÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25Se admite cotización parcial por renglónContratoMiguel Lillo N° 251, S.M. de Tucumán, Tucumán30 Días corridos Acto de aperturaNoApartado 8: Adjudicación Simple InteradministrativaÚnicaAutorización del pliegoAutorización de llamadoNaNNoNoNoNaNNaN16/05/2022 08:30 Hrs.16/05/2022 09:00 Hrs.16/05/2022 10:00 Hrs.NaNNaNNaN16/05/2022 11:00 Hrs.A partir del perfeccionamiento del documento contractual6 Meses0.00.0
7051098-0027-CDI22EX-2022-108159624- -APN-DA#FMLCAVSERVICIO DE CAMBIO DE ALFOMBRA EN EL CENTRO CULTURAL.-Contratación Directa04/11/2022 11:00 Hrs.Desierto98/00 - Dir. Compras101 - Fundación Miguel LilloEX-2022-108159624- -APN-DA#FMLCAVÚnicaSin modalidadPeso ArgentinoDecreto Delegado N° 1023/2001 Art. 25 Decreto N°1030/2016 Art.14No admite cotización parcial por renglónOrden de compraMiguel Lillo N° 251, S.M. de Tucumán, Tucumán30 Días corridos Acto de aperturaNoApartado 1: Compulsa Abreviada Por MontoÚnicaAutorización del pliegoAutorización de llamadoActo de aperturaNoNoNoNaNNaN19/10/2022 13:00 Hrs.19/10/2022 16:00 Hrs.28/10/2022 09:00 Hrs.NaNNaNNaN04/11/2022 11:00 Hrs.A partir del perfeccionamiento del documento contractual30 Días hábiles0.00.0